Solar EUV Spectral Irradiance by Deep Learning

Extreme UV (EUV) radiation from the Sun's transition region and corona is an important driver for the energy balance of the Earth's thermosphere and ionosphere. To characterise and monitor solar forcing on this system and associated space weather impacts, the EUV Variability Experiment (EVE) instrument onboard NASA's Solar Dynamics Observatory (SDO) was designed to measure solar spectral irradiance (SSI) in the 0.1 to 105 nm wavelength range. As the result of an electrical short, the MEGS-A component of EVE stopped delivering SSI data in the 5 - 35 nm wavelength range in May 2014. We demonstrate how a Residual Neural Network (ResNet) augmented with a Multi-Layer Perceptron (MLP) can fill this gap using narrowband UV and EUV images from the Atmospheric Imaging Assembly (AIA) on SDO. As a performance benchmark, we also show how our deep learning approach outperforms a physics model based on differential emission measure inversions. This work was performed at NASA's Frontier Development Lab, a public-private initiative to apply AI techniques to accelerate space science discovery and exploration.